public static IActivationFunction GetActivationFunction(ActivationFunction activationFunction) { IActivationFunction result; switch (activationFunction) { case ActivationFunction.ELU: result = new ELU(); break; case ActivationFunction.ReLU: result = new ReLU(); break; case ActivationFunction.Sigmoid: result = new Sigmoid(); break; case ActivationFunction.None: result = null; break; case ActivationFunction.Swish: result = new Swish(); break; default: result = new ReLU(); break; } return(result); }
private void btn_createNetwork_Click(object sender, RoutedEventArgs e) { int[] layers = new int[this.wp_layers.Items.Count + 2]; layers[0] = Convert.ToInt32((this.ucInputLayer.Content as NetwokLayerSizeControl).GetLayerSize()); for (int i = 1; i < layers.Length - 1; i++) { layers[i] = Convert.ToInt32((this.wp_layers.Items[i - 1] as NetwokLayerSizeControl).GetLayerSize()); } layers[layers.Length - 1] = Convert.ToInt32((this.ucOutputLayer.Content as NetwokLayerSizeControl).GetLayerSize()); ActivationStrategy acti = new Sigmoid(); if (this.cb_activationFunctions.SelectedIndex == 0) { acti = new Sigmoid(); } else if (this.cb_activationFunctions.SelectedIndex == 1) { acti = new TanH(); } else if (this.cb_activationFunctions.SelectedIndex == 2) { acti = new ReLU(); } NetworkHelper.SaveNetToFile(new NeuralNetwork(layers, acti), "neuralNetwork.txt"); MessageBox.Show("Success!"); }
public void CalculateActivationTest(float input, float expected) { float epsilon = 0.0001f; ActivationFunction activation = new ReLU(); float result = activation.CalculateActivation(input); Assert.IsTrue(Math.Abs(result - expected) < epsilon, $"Expected value <{expected}>. Actual <{result}>"); }
public static ushort EnCodeIConduction(IConduction conduction) { return(conduction switch { Sigmoind _ => 1, ReLU _ => 2, SoftReLU _ => 3, Straight _ => 4, SoftMax _ => 5, _ => throw new ArgumentException( nameof(conduction), $"this type of IConduction is not registered. {conduction}"), });
public static void Run() { sw = new Stopwatch(); Console.WriteLine("Generating Test Data..."); NdArray input = new NdArray(BenchDataMaker.GetRealArray(INPUT_SIZE)); NdArray inputImage = new NdArray(BenchDataMaker.GetRealArray(3 * 256 * 256 * 5), new[] { 3, 256, 256 }, 5); Console.WriteLine("Generated Test Data"); Console.WriteLine("Init Linear"); Linear linear = new Linear(INPUT_SIZE, OUTPUT_SIZE); Console.WriteLine("Init Tanh"); Tanh tanh = new Tanh(); Console.WriteLine("Init Sigmoid"); Sigmoid sigmoid = new Sigmoid(); Console.WriteLine("Init ReLU"); ReLU relu = new ReLU(); Console.WriteLine("Init LeakyReLU"); LeakyReLU leakyRelu = new LeakyReLU(); Console.WriteLine("Init MaxPooling"); MaxPooling maxPooling = new MaxPooling(2); Console.WriteLine("Init Convolution2D"); Convolution2D conv2d = new Convolution2D(3, 32, 3); Console.WriteLine("Init Deconvolution2D"); Deconvolution2D deconv2d = new Deconvolution2D(32, 3, 3); Dropout dropout = new Dropout(); TestLayer(linear, input); Console.WriteLine("aaaaaaaaaaaa"); Console.ReadLine(); TestLayer(tanh, input); TestLayer(sigmoid, input); TestLayer(relu, input); TestLayer(leakyRelu, input); TestLayer(maxPooling, inputImage); TestLayer(conv2d, inputImage); TestLayer(deconv2d, inputImage); TestLayer(dropout, input); }
public void ReLUTest() { var input = new TensorOld(new double[] { 1, 2, -3, 5, -2, 7, 4, 6, 8, -5, 4, 1 }, 3, 4); var expected = new TensorOld(new double[] { 1, 2, 0, 5, 0, 7, 4, 6, 8, 0, 4, 1 }, 3, 4); var relu = new ReLU(); relu.PrepareTrain(input); var actual = relu.Forward(input); Assert.Equal(expected, actual); var error = TensorOld.Values(0.1, 3, 4); var expError = new TensorOld(new double[] { 0.1, 0.1, 0, 0.1, 0, 0.1, 0.1, 0.1, 0.1, 0, 0.1, 0.1 }, 3, 4); var actError = relu.Backward(error); Assert.Equal(expError, actError); }
static public List <Layer> BuildFromConfig(ModelConfig c, double mCenter = 0, double mOffset = 1, double bCenter = 0, double bOffset = 1) { int deep = c.layers.Count - 1; List <Layer> layers = new List <Layer>(deep); ReLU layer = new ReLU(); if (deep <= 1) { throw new Exception("Too few layers!"); } else { layers.Add(BuildRandom(c.layers[0].w, c.layers[1].w, layer, mCenter, mOffset, bCenter, bOffset)); for (int i = 1; i < deep - 1; i++) { layers.Add(BuildRandom(c.layers[i].w, c.layers[i + 1].w, layer, mCenter, mOffset, bCenter, bOffset)); } layers.Add(BuildRandom(c.layers[deep - 1].w, c.layers[deep].w, layer, mCenter, mOffset, bCenter, bOffset)); } return(layers); }
private Sequential CreateSequential(List <String> model) { // TODO just assumes it is all in a seq model the seq model should probably // be in the JSON???? var seq = new Sequential(controller); foreach (var l in model) { var config = JObject.Parse(l); Layer layer = null; switch ((string)config["name"]) { case "linear": layer = new Linear(controller, (int)config["input"], (int)config["output"]); break; case "softmax": layer = new Softmax(controller, (int)config["dim"]); break; case "relu": layer = new ReLU(controller); break; case "log": layer = new Log(controller); break; case "dropout": layer = new Dropout(controller, (float)config["rate"]); break; } seq.AddLayer(layer); } return(seq); }
public static void Run() { Stopwatch sw = new Stopwatch(); NdArray inputArrayCpu = new NdArray(Initializer.GetRealArray(INPUT_SIZE)); NdArray inputArrayGpu = new NdArray(Initializer.GetRealArray(INPUT_SIZE)); //Linear Linear linear = new Linear(INPUT_SIZE, OUTPUT_SIZE); Console.WriteLine("◆" + linear.Name); sw.Restart(); NdArray[] gradArrayCpu = linear.Forward(inputArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; //DataをGradとして使用 sw.Restart(); linear.Backward(gradArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (linear.SetGpuEnable(true)) { sw.Restart(); NdArray[] gradArrayGpu = linear.Forward(inputArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayGpu[0].Grad = gradArrayGpu[0].Data; sw.Restart(); linear.Backward(gradArrayGpu); sw.Stop(); Console.WriteLine("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } //Tanh TanhActivation tanh = new TanhActivation(); Console.WriteLine("\n◆" + tanh.Name); sw.Restart(); gradArrayCpu = tanh.Forward(inputArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); tanh.Backward(gradArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (tanh.SetGpuEnable(true)) { sw.Restart(); NdArray[] gradArrayGpu = tanh.Forward(inputArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayGpu[0].Grad = gradArrayGpu[0].Data; sw.Restart(); tanh.Backward(gradArrayGpu); sw.Stop(); Console.WriteLine("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } //Sigmoid Sigmoid sigmoid = new Sigmoid(); Console.WriteLine("\n◆" + sigmoid.Name); sw.Restart(); gradArrayCpu = sigmoid.Forward(inputArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); sigmoid.Backward(gradArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (sigmoid.SetGpuEnable(true)) { sw.Restart(); NdArray[] gradArrayGpu = sigmoid.Forward(inputArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayGpu[0].Grad = gradArrayGpu[0].Data; sw.Restart(); sigmoid.Backward(gradArrayGpu); sw.Stop(); Console.WriteLine("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } //ReLU ReLU relu = new ReLU(); Console.WriteLine("\n◆" + relu.Name); sw.Restart(); gradArrayCpu = relu.Forward(inputArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); relu.Backward(gradArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (relu.SetGpuEnable(true)) { sw.Restart(); NdArray[] gradArrayGpu = relu.Forward(inputArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayGpu[0].Grad = gradArrayGpu[0].Data; sw.Restart(); relu.Backward(gradArrayGpu); sw.Stop(); Console.WriteLine("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } //LeakyReLU LeakyReLU leakyRelu = new LeakyReLU(); Console.WriteLine("\n◆" + leakyRelu.Name); sw.Restart(); gradArrayCpu = leakyRelu.Forward(inputArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); leakyRelu.Backward(gradArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (leakyRelu.SetGpuEnable(true)) { sw.Restart(); NdArray[] gradArrayGpu = leakyRelu.Forward(inputArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayGpu[0].Grad = gradArrayGpu[0].Data; sw.Restart(); leakyRelu.Backward(gradArrayGpu); sw.Stop(); Console.WriteLine("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } NdArray inputImageArrayGpu = new NdArray(Initializer.GetRealArray(3 * 256 * 256 * 5), new[] { 3, 256, 256 }, 5); NdArray inputImageArrayCpu = new NdArray(Initializer.GetRealArray(3 * 256 * 256 * 5), new[] { 3, 256, 256 }, 5); //MaxPooling MaxPooling2D maxPooling2D = new MaxPooling2D(3); Console.WriteLine("\n◆" + maxPooling2D.Name); sw.Restart(); NdArray[] gradImageArrayCpu = maxPooling2D.Forward(inputImageArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradImageArrayCpu[0].Grad = gradImageArrayCpu[0].Data; sw.Restart(); maxPooling2D.Backward(gradImageArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (maxPooling2D.SetGpuEnable(true)) { sw.Restart(); maxPooling2D.Forward(inputImageArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); //メモリ転送のみのため実装がない Console.WriteLine("Backward[Gpu] : None"); } //Conv2D Convolution2D conv2d = new Convolution2D(3, 3, 3); Console.WriteLine("\n◆" + conv2d.Name); sw.Restart(); gradImageArrayCpu = conv2d.Forward(inputImageArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradImageArrayCpu[0].Grad = gradImageArrayCpu[0].Data; sw.Restart(); conv2d.Backward(gradImageArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (conv2d.SetGpuEnable(true)) { sw.Restart(); NdArray[] gradImageArrayGpu = conv2d.Forward(inputImageArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradImageArrayGpu[0].Grad = gradImageArrayGpu[0].Data; sw.Restart(); conv2d.Backward(gradImageArrayGpu); sw.Stop(); Console.WriteLine("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } //Deconv2D Deconvolution2D deconv2d = new Deconvolution2D(3, 3, 3); Console.WriteLine("\n◆" + deconv2d.Name); sw.Restart(); gradImageArrayCpu = deconv2d.Forward(inputImageArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradImageArrayCpu[0].Grad = gradImageArrayCpu[0].Data; sw.Restart(); deconv2d.Backward(gradImageArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (deconv2d.SetGpuEnable(true)) { sw.Restart(); NdArray[] gradImageArrayGpu = deconv2d.Forward(inputImageArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradImageArrayGpu[0].Grad = gradImageArrayGpu[0].Data; sw.Restart(); deconv2d.Backward(gradImageArrayGpu); sw.Stop(); Console.WriteLine("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } //Dropout Dropout dropout = new Dropout(); Console.WriteLine("\n◆" + dropout.Name); sw.Restart(); gradArrayCpu = dropout.Forward(inputArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); dropout.Backward(gradArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (dropout.SetGpuEnable(true)) { sw.Restart(); NdArray[] gradArrayGpu = dropout.Forward(inputArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayGpu[0].Grad = gradArrayGpu[0].Data; sw.Restart(); dropout.Backward(gradArrayGpu); sw.Stop(); Console.WriteLine("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } }
public Layer(int nCount, int index, ActivationSettings activationSettings) { NCount = nCount; Index = index; ActivationType = activationSettings.Type(); // Activation Setup switch (activationSettings.Type()) { case EActivationType.Invalid: Activation = null; throw new ArgumentException("Activation Type Invalid."); case EActivationType.Arctan: Activation = new Arctan(); break; case EActivationType.BinaryStep: Activation = new BinaryStep(); break; case EActivationType.BipolarSigmoid: Activation = new BipolarSigmoid(); break; case EActivationType.ELU: Activation = new ELU((ELUSettings)activationSettings); break; case EActivationType.HardSigmoid: Activation = new HardSigmoid(); break; case EActivationType.HardTanh: Activation = new HardTanh(); break; case EActivationType.Identity: Activation = new Identity(); break; case EActivationType.Logit: Activation = new Logit(); break; case EActivationType.LReLU: Activation = new LReLU((LReLUSettings)activationSettings); break; case EActivationType.Mish: Activation = new Mish(); break; case EActivationType.ReLU: Activation = new ReLU(); break; case EActivationType.SeLU: Activation = new SeLU(); break; case EActivationType.Sigmoid: Activation = new Sigmoid(); break; case EActivationType.Softmax: Activation = new Softmax(); break; case EActivationType.Softplus: Activation = new Softplus(); break; case EActivationType.Softsign: Activation = new Softsign(); break; case EActivationType.Tanh: Activation = new Tanh(); break; default: throw new ArgumentException("Activation Type Invalid."); } }
internal virtual Variable Activate(Variable x) { return(ReLU.ForwardStatic(x)); }
public static void Run(bool verbose) { Stopwatch sw = new Stopwatch(); NdArray inputArrayCpu = new NdArray(BenchDataMaker.GetRealArray(INPUT_SIZE)); NdArray inputArrayGpu = new NdArray(BenchDataMaker.GetRealArray(INPUT_SIZE)); Ensure.Argument(inputArrayGpu).NotNull(); Ensure.Argument(inputArrayCpu).NotNull(); //Linear Linear linear = new Linear(verbose, INPUT_SIZE, OUTPUT_SIZE); if (verbose) { RILogManager.Default?.EnterMethod(linear.Name); } sw.Restart(); NdArray[] gradArrayCpu = linear.Forward(verbose, inputArrayCpu); sw.Stop(); if (verbose) { RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } Ensure.Argument(gradArrayCpu).NotNull(); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; // Use Data as Grad sw.Restart(); linear.Backward(verbose, gradArrayCpu); sw.Stop(); if (verbose) { RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } if (linear.SetGpuEnable(true)) { sw.Restart(); NdArray[] gradArrayGpu = linear.Forward(verbose, inputArrayGpu); sw.Stop(); if (verbose) { RILogManager.Default?.SendDebug("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } gradArrayGpu[0].Grad = gradArrayGpu[0].Data; sw.Restart(); linear.Backward(verbose, gradArrayGpu); sw.Stop(); if (verbose) { RILogManager.Default?.SendDebug("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } } if (verbose) { RILogManager.Default?.ExitMethod(linear.Name); } //Tanh Tanh tanh = new Tanh(); if (verbose) { RILogManager.Default?.EnterMethod(tanh.Name); } sw.Restart(); gradArrayCpu = tanh.Forward(verbose, inputArrayCpu); sw.Stop(); if (verbose) { RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); tanh.Backward(verbose, gradArrayCpu); sw.Stop(); if (verbose) { RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } if (tanh.SetGpuEnable(true)) { HandleGPU(verbose, sw, tanh, inputArrayGpu); } if (verbose) { RILogManager.Default?.ExitMethod(tanh.Name); } //Sigmoid Sigmoid sigmoid = new Sigmoid(); if (verbose) { RILogManager.Default?.EnterMethod(sigmoid.Name); } sw.Restart(); gradArrayCpu = sigmoid.Forward(verbose, inputArrayCpu); sw.Stop(); if (verbose) { RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); sigmoid.Backward(verbose, gradArrayCpu); sw.Stop(); if (verbose) { RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } if (sigmoid.SetGpuEnable(true)) { HandleGPU(verbose, sw, sigmoid, inputArrayGpu); } if (verbose) { RILogManager.Default?.ExitMethod(tanh.Name); } //Softmax Softmax sm = new Softmax(); RILogManager.Default?.EnterMethod(sm.Name); sw.Restart(); gradArrayCpu = sm.Forward(verbose, inputArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); sm.Backward(verbose, gradArrayCpu); sw.Stop(); if (verbose) { RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } if (verbose) { RILogManager.Default?.ExitMethod(sm.Name); } //Softplus Softplus sp = new Softplus(); if (verbose) { RILogManager.Default?.EnterMethod(sp.Name); } sw.Restart(); gradArrayCpu = sp.Forward(verbose, inputArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); sp.Backward(verbose, gradArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); RILogManager.Default?.ExitMethod(sp.Name); //ReLU ReLU relu = new ReLU(); RILogManager.Default?.EnterMethod(relu.Name); sw.Restart(); gradArrayCpu = relu.Forward(verbose, inputArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); relu.Backward(verbose, gradArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (relu.SetGpuEnable(true)) { HandleGPU(verbose, sw, relu, inputArrayGpu); } RILogManager.Default?.ExitMethod(relu.Name); //LeakyReLU LeakyReLU leakyRelu = new LeakyReLU(); RILogManager.Default?.EnterMethod(leakyRelu.Name); sw.Restart(); gradArrayCpu = leakyRelu.Forward(verbose, inputArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); leakyRelu.Backward(verbose, gradArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (leakyRelu.SetGpuEnable(true)) { HandleGPU(verbose, sw, leakyRelu, inputArrayGpu); } RILogManager.Default?.ExitMethod(leakyRelu.Name); //ReLuTanh ReLuTanh rth = new ReLuTanh(); RILogManager.Default?.EnterMethod(rth.Name); sw.Restart(); gradArrayCpu = rth.Forward(verbose, inputArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); rth.Backward(verbose, gradArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (rth.SetGpuEnable(true)) { HandleGPU(verbose, sw, rth, inputArrayGpu); } RILogManager.Default?.ExitMethod(rth.Name); ////Swish //Swish swi = new Swish(); //RILogManager.Default?.SendDebug(swi.Name); //sw.Restart(); //gradArrayCpu = swi.Forward(inputArrayCpu); //sw.Stop(); //RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); //gradArrayCpu[0].Grad = gradArrayCpu[0].Data; //sw.Restart(); //swi.Backward(gradArrayCpu); //sw.Stop(); //RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); NdArray inputImageArrayGpu = new NdArray(BenchDataMaker.GetRealArray(3 * 256 * 256 * 5), new[] { 3, 256, 256 }, 5); NdArray inputImageArrayCpu = new NdArray(BenchDataMaker.GetRealArray(3 * 256 * 256 * 5), new[] { 3, 256, 256 }, 5); //MaxPooling MaxPooling maxPooling = new MaxPooling(3); RILogManager.Default?.EnterMethod(maxPooling.Name); sw.Restart(); NdArray[] gradImageArrayCpu = maxPooling.Forward(verbose, inputImageArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradImageArrayCpu[0].Grad = gradImageArrayCpu[0].Data; sw.Restart(); maxPooling.Backward(verbose, gradImageArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (maxPooling.SetGpuEnable(true)) { sw.Restart(); maxPooling.Forward(verbose, inputImageArrayGpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); // There is no implementation for memory transfer only RILogManager.Default?.SendDebug("Backward[Gpu] : None"); } RILogManager.Default?.ExitMethod(maxPooling.Name); //AvgPooling AveragePooling avgPooling = new AveragePooling(3); RILogManager.Default?.EnterMethod(avgPooling.Name); sw.Restart(); gradImageArrayCpu = avgPooling.Forward(verbose, inputImageArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradImageArrayCpu[0].Grad = gradImageArrayCpu[0].Data; sw.Restart(); avgPooling.Backward(verbose, gradImageArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); RILogManager.Default?.ExitMethod(avgPooling.Name); //Conv2D Convolution2D conv2d = new Convolution2D(verbose, 3, 3, 3); RILogManager.Default?.EnterMethod(conv2d.Name); sw.Restart(); gradImageArrayCpu = conv2d.Forward(verbose, inputImageArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradImageArrayCpu[0].Grad = gradImageArrayCpu[0].Data; sw.Restart(); conv2d.Backward(verbose, gradImageArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (conv2d.SetGpuEnable(true)) { HandleGPU(verbose, sw, conv2d, inputArrayGpu); } RILogManager.Default?.ExitMethod(conv2d.Name); //Deconv2D Deconvolution2D deconv2d = new Deconvolution2D(verbose, 3, 3, 3); RILogManager.Default?.EnterMethod(deconv2d.Name); sw.Restart(); gradImageArrayCpu = deconv2d.Forward(verbose, inputImageArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradImageArrayCpu[0].Grad = gradImageArrayCpu[0].Data; sw.Restart(); deconv2d.Backward(verbose, gradImageArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (deconv2d.SetGpuEnable(true)) { HandleGPU(verbose, sw, deconv2d, inputArrayGpu); } RILogManager.Default?.ExitMethod(deconv2d.Name); //Dropout Dropout dropout = new Dropout(); RILogManager.Default?.EnterMethod(dropout.Name); sw.Restart(); gradArrayCpu = dropout.Forward(verbose, inputArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); dropout.Backward(verbose, gradArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (dropout.SetGpuEnable(true)) { sw.Restart(); NdArray[] gradArrayGpu = dropout.Forward(verbose, inputArrayGpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayGpu[0].Grad = gradArrayGpu[0].Data; sw.Restart(); dropout.Backward(verbose, gradArrayGpu); sw.Stop(); RILogManager.Default?.SendDebug("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } RILogManager.Default?.ExitMethod(dropout.Name); //ArcSinH ArcSinH a = new ArcSinH(); RILogManager.Default?.EnterMethod(a.Name); sw.Restart(); gradArrayCpu = a.Forward(verbose, inputArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); a.Backward(verbose, gradArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (a.SetGpuEnable(true)) { HandleGPU(verbose, sw, a, inputArrayGpu); } RILogManager.Default?.ExitMethod(a.Name); //ELU ELU e = new ELU(); RILogManager.Default?.EnterMethod(e.Name); sw.Restart(); gradArrayCpu = e.Forward(verbose, inputArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); e.Backward(verbose, gradArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); RILogManager.Default?.ExitMethod(e.Name); //LeakyReluShifted LeakyReLUShifted lrs = new LeakyReLUShifted(); RILogManager.Default?.EnterMethod(lrs.Name); sw.Restart(); gradArrayCpu = lrs.Forward(verbose, inputArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); lrs.Backward(verbose, gradArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (lrs.SetGpuEnable(true)) { HandleGPU(verbose, sw, lrs, inputArrayGpu); } RILogManager.Default?.ExitMethod(lrs.Name); //Logistic LogisticFunction lf = new LogisticFunction(); RILogManager.Default?.EnterMethod(lf.Name); sw.Restart(); gradArrayCpu = lf.Forward(verbose, inputArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); lf.Backward(verbose, gradArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (lf.SetGpuEnable(true)) { HandleGPU(verbose, sw, lf, inputArrayGpu); } RILogManager.Default?.ExitMethod(lf.Name); //MaxMinusOne MaxMinusOne mmo = new MaxMinusOne(); RILogManager.Default?.EnterMethod(mmo.Name); sw.Restart(); gradArrayCpu = mmo.Forward(verbose, inputArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); mmo.Backward(verbose, gradArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (mmo.SetGpuEnable(true)) { HandleGPU(verbose, sw, mmo, inputArrayGpu); } RILogManager.Default?.ExitMethod(mmo.Name); //ScaledELU ScaledELU se = new ScaledELU(); RILogManager.Default?.EnterMethod(se.Name); sw.Restart(); gradArrayCpu = se.Forward(verbose, inputArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); se.Backward(verbose, gradArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (se.SetGpuEnable(true)) { HandleGPU(verbose, sw, se, inputArrayGpu); } RILogManager.Default?.ExitMethod(se.Name); //Sine Sine s = new Sine(); RILogManager.Default?.EnterMethod(s.Name); sw.Restart(); gradArrayCpu = s.Forward(verbose, inputArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); s.Backward(verbose, gradArrayCpu); sw.Stop(); RILogManager.Default?.SendDebug("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (s.SetGpuEnable(true)) { HandleGPU(verbose, sw, s, inputArrayGpu); } RILogManager.Default?.ExitMethod(s.Name); }
public void ProcessMessage(string json_message, MonoBehaviour owner, Action <string> response) { Command msgObj = JsonUtility.FromJson <Command> (json_message); try { switch (msgObj.objectType) { case "Optimizer": { if (msgObj.functionCall == "create") { string optimizer_type = msgObj.tensorIndexParams[0]; // Extract parameters List <int> p = new List <int>(); for (int i = 1; i < msgObj.tensorIndexParams.Length; i++) { p.Add(int.Parse(msgObj.tensorIndexParams[i])); } List <float> hp = new List <float>(); for (int i = 0; i < msgObj.hyperParams.Length; i++) { hp.Add(float.Parse(msgObj.hyperParams[i])); } Optimizer optim = null; if (optimizer_type == "sgd") { optim = new SGD(this, p, hp[0], hp[1], hp[2]); } else if (optimizer_type == "rmsprop") { optim = new RMSProp(this, p, hp[0], hp[1], hp[2], hp[3]); } else if (optimizer_type == "adam") { optim = new Adam(this, p, hp[0], hp[1], hp[2], hp[3], hp[4]); } response(optim.Id.ToString()); return; } else { Optimizer optim = this.GetOptimizer(msgObj.objectIndex); response(optim.ProcessMessage(msgObj, this)); return; } } case "FloatTensor": { if (msgObj.objectIndex == 0 && msgObj.functionCall == "create") { FloatTensor tensor = floatTensorFactory.Create(_shape: msgObj.shape, _data: msgObj.data, _shader: this.Shader); response(tensor.Id.ToString()); return; } else { FloatTensor tensor = floatTensorFactory.Get(msgObj.objectIndex); // Process message's function response(tensor.ProcessMessage(msgObj, this)); return; } } case "IntTensor": { if (msgObj.objectIndex == 0 && msgObj.functionCall == "create") { int[] data = new int[msgObj.data.Length]; for (int i = 0; i < msgObj.data.Length; i++) { data[i] = (int)msgObj.data[i]; } IntTensor tensor = intTensorFactory.Create(_shape: msgObj.shape, _data: data); response(tensor.Id.ToString()); return; } else { IntTensor tensor = intTensorFactory.Get(msgObj.objectIndex); // Process message's function response(tensor.ProcessMessage(msgObj, this)); return; } } case "agent": { if (msgObj.functionCall == "create") { Layer model = (Layer)GetModel(int.Parse(msgObj.tensorIndexParams[0])); Optimizer optimizer = optimizers[int.Parse(msgObj.tensorIndexParams[1])]; response(new Syft.NN.RL.Agent(this, model, optimizer).Id.ToString()); return; } //Debug.Log("Getting Model:" + msgObj.objectIndex); Syft.NN.RL.Agent agent = this.GetAgent(msgObj.objectIndex); response(agent.ProcessMessageLocal(msgObj, this)); return; } case "model": { if (msgObj.functionCall == "create") { string model_type = msgObj.tensorIndexParams[0]; Debug.LogFormat("<color=magenta>createModel:</color> {0}", model_type); if (model_type == "linear") { response(this.BuildLinear(msgObj.tensorIndexParams).Id.ToString()); return; } else if (model_type == "relu") { response(this.BuildReLU().Id.ToString()); return; } else if (model_type == "log") { response(this.BuildLog().Id.ToString()); return; } else if (model_type == "dropout") { response(this.BuildDropout(msgObj.tensorIndexParams).Id.ToString()); return; } else if (model_type == "sigmoid") { response(this.BuildSigmoid().Id.ToString()); return; } else if (model_type == "sequential") { response(this.BuildSequential().Id.ToString()); return; } else if (model_type == "softmax") { response(this.BuildSoftmax(msgObj.tensorIndexParams).Id.ToString()); return; } else if (model_type == "logsoftmax") { response(this.BuildLogSoftmax(msgObj.tensorIndexParams).Id.ToString()); return; } else if (model_type == "tanh") { response(new Tanh(this).Id.ToString()); return; } else if (model_type == "crossentropyloss") { response(new CrossEntropyLoss(this, int.Parse(msgObj.tensorIndexParams[1])).Id.ToString()); return; } else if (model_type == "categorical_crossentropy") { response(new CategoricalCrossEntropyLoss(this).Id.ToString()); return; } else if (model_type == "nllloss") { response(new NLLLoss(this).Id.ToString()); return; } else if (model_type == "mseloss") { response(new MSELoss(this).Id.ToString()); return; } else if (model_type == "embedding") { response(new Embedding(this, int.Parse(msgObj.tensorIndexParams[1]), int.Parse(msgObj.tensorIndexParams[2])).Id.ToString()); return; } else { Debug.LogFormat("<color=red>Model Type Not Found:</color> {0}", model_type); } } else { //Debug.Log("Getting Model:" + msgObj.objectIndex); Model model = this.GetModel(msgObj.objectIndex); response(model.ProcessMessage(msgObj, this)); return; } response("Unity Error: SyftController.processMessage: Command not found:" + msgObj.objectType + ":" + msgObj.functionCall); return; } case "controller": { if (msgObj.functionCall == "num_tensors") { response(floatTensorFactory.Count() + ""); return; } else if (msgObj.functionCall == "num_models") { response(models.Count + ""); return; } else if (msgObj.functionCall == "new_tensors_allowed") { Debug.LogFormat("New Tensors Allowed:{0}", msgObj.tensorIndexParams[0]); if (msgObj.tensorIndexParams[0] == "True") { allow_new_tensors = true; } else if (msgObj.tensorIndexParams[0] == "False") { allow_new_tensors = false; } else { throw new Exception("Invalid parameter for new_tensors_allowed. Did you mean true or false?"); } response(allow_new_tensors + ""); return; } else if (msgObj.functionCall == "load_floattensor") { FloatTensor tensor = floatTensorFactory.Create(filepath: msgObj.tensorIndexParams[0], _shader: this.Shader); response(tensor.Id.ToString()); return; } else if (msgObj.functionCall == "set_seed") { Random.InitState(int.Parse(msgObj.tensorIndexParams[0])); response("Random seed set!"); return; } else if (msgObj.functionCall == "concatenate") { List <int> tensor_ids = new List <int>(); for (int i = 1; i < msgObj.tensorIndexParams.Length; i++) { tensor_ids.Add(int.Parse(msgObj.tensorIndexParams[i])); } FloatTensor result = Functional.Concatenate(floatTensorFactory, tensor_ids, int.Parse(msgObj.tensorIndexParams[0])); response(result.Id.ToString()); return; } else if (msgObj.functionCall == "ones") { int[] dims = new int[msgObj.tensorIndexParams.Length]; for (int i = 0; i < msgObj.tensorIndexParams.Length; i++) { dims[i] = int.Parse(msgObj.tensorIndexParams[i]); } FloatTensor result = Functional.Ones(floatTensorFactory, dims); response(result.Id.ToString()); return; } else if (msgObj.functionCall == "randn") { int[] dims = new int[msgObj.tensorIndexParams.Length]; for (int i = 0; i < msgObj.tensorIndexParams.Length; i++) { dims[i] = int.Parse(msgObj.tensorIndexParams[i]); } FloatTensor result = Functional.Randn(floatTensorFactory, dims); response(result.Id.ToString()); return; } else if (msgObj.functionCall == "random") { int[] dims = new int[msgObj.tensorIndexParams.Length]; for (int i = 0; i < msgObj.tensorIndexParams.Length; i++) { dims[i] = int.Parse(msgObj.tensorIndexParams[i]); } FloatTensor result = Functional.Random(floatTensorFactory, dims); response(result.Id.ToString()); return; } else if (msgObj.functionCall == "zeros") { int[] dims = new int[msgObj.tensorIndexParams.Length]; for (int i = 0; i < msgObj.tensorIndexParams.Length; i++) { dims[i] = int.Parse(msgObj.tensorIndexParams[i]); } FloatTensor result = Functional.Zeros(floatTensorFactory, dims); response(result.Id.ToString()); return; } else if (msgObj.functionCall == "model_from_json") { Debug.Log("Loading Model from JSON:"); var json_str = msgObj.tensorIndexParams[0]; var config = JObject.Parse(json_str); Sequential model; if ((string)config["class_name"] == "Sequential") { model = this.BuildSequential(); } else { response("Unity Error: SyftController.processMessage: while Loading model, Class :" + config["class_name"] + " is not implemented"); return; } for (int i = 0; i < config["config"].ToList().Count; i++) { var layer_desc = config["config"][i]; var layer_config_desc = layer_desc["config"]; if ((string)layer_desc["class_name"] == "Linear") { int previous_output_dim; if (i == 0) { previous_output_dim = (int)layer_config_desc["batch_input_shape"][layer_config_desc["batch_input_shape"].ToList().Count - 1]; } else { previous_output_dim = (int)layer_config_desc["units"]; } string[] parameters = { "linear", previous_output_dim.ToString(), layer_config_desc["units"].ToString(), "Xavier" }; Layer layer = this.BuildLinear(parameters); model.AddLayer(layer); string activation_name = layer_config_desc["activation"].ToString(); if (activation_name != "linear") { Layer activation; if (activation_name == "softmax") { parameters = new string[] { activation_name, "1" }; activation = this.BuildSoftmax(parameters); } else if (activation_name == "relu") { activation = this.BuildReLU(); } else { response("Unity Error: SyftController.processMessage: while Loading activations, Activation :" + activation_name + " is not implemented"); return; } model.AddLayer(activation); } } else { response("Unity Error: SyftController.processMessage: while Loading layers, Layer :" + layer_desc["class_name"] + " is not implemented"); return; } } response(model.Id.ToString()); return; } else if (msgObj.functionCall == "from_proto") { Debug.Log("Loading Model from ONNX:"); var filename = msgObj.tensorIndexParams[0]; var input = File.OpenRead(filename); ModelProto modelProto = ModelProto.Parser.ParseFrom(input); Sequential model = this.BuildSequential(); foreach (NodeProto node in modelProto.Graph.Node) { Layer layer; GraphProto g = ONNXTools.GetSubGraphFromNodeAndMainGraph(node, modelProto.Graph); if (node.OpType == "Gemm") { layer = new Linear(this, g); } else if (node.OpType == "Dropout") { layer = new Dropout(this, g); } else if (node.OpType == "Relu") { layer = new ReLU(this, g); } else if (node.OpType == "Softmax") { layer = new Softmax(this, g); } else { response("Unity Error: SyftController.processMessage: Layer not yet implemented for deserialization:"); return; } model.AddLayer(layer); } response(model.Id.ToString()); return; } else if (msgObj.functionCall == "to_proto") { ModelProto model = this.ToProto(msgObj.tensorIndexParams); string filename = msgObj.tensorIndexParams[2]; string type = msgObj.tensorIndexParams[3]; if (type == "json") { response(model.ToString()); } else { using (var output = File.Create(filename)) { model.WriteTo(output); } response(new FileInfo(filename).FullName); } return; } response("Unity Error: SyftController.processMessage: Command not found:" + msgObj.objectType + ":" + msgObj.functionCall); return; } case "Grid": if (msgObj.functionCall == "learn") { var inputId = int.Parse(msgObj.tensorIndexParams[0]); var targetId = int.Parse(msgObj.tensorIndexParams[1]); response(this.grid.Run(inputId, targetId, msgObj.configurations, owner)); return; } if (msgObj.functionCall == "getResults") { this.grid.GetResults(msgObj.experimentId, response); return; } // like getResults but doesn't pause to wait for results // this function will return right away telling you if // it knows whether or not it is done if (msgObj.functionCall == "checkStatus") { this.grid.CheckStatus(msgObj.experimentId, response); return; } break; default: break; } } catch (Exception e) { Debug.LogFormat("<color=red>{0}</color>", e.ToString()); response("Unity Error: " + e.ToString()); return; } // If not executing createTensor or tensor function, return default error. response("Unity Error: SyftController.processMessage: Command not found:" + msgObj.objectType + ":" + msgObj.functionCall); return; }
public static void Run() { Stopwatch sw = new Stopwatch(); NdArray inputArrayCpu = new NdArray(BenchDataMaker.GetRealArray(INPUT_SIZE)); NdArray inputArrayGpu = new NdArray(BenchDataMaker.GetRealArray(INPUT_SIZE)); NdArray[] gradArrayCpu = null; //Linear Linear linear = new Linear(INPUT_SIZE, OUTPUT_SIZE); Console.WriteLine("◆" + linear.Name); if (TestCpu) { sw.Restart(); gradArrayCpu = linear.Forward(inputArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; //Use Data as Grad sw.Restart(); linear.Backward(gradArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } if (linear.SetGpuEnable(true)) { NdArray[] gradArrayGpu = null; while (true) { sw.Restart(); gradArrayGpu = linear.Forward(inputArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } gradArrayGpu[0].Grad = gradArrayGpu[0].Data; sw.Restart(); linear.Backward(gradArrayGpu); sw.Stop(); Console.WriteLine("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } //Tanh Tanh tanh = new Tanh(); Console.WriteLine("\n ◆" + tanh.Name); sw.Restart(); gradArrayCpu = tanh.Forward(inputArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); tanh.Backward(gradArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (tanh.SetGpuEnable(true)) { sw.Restart(); NdArray[] gradArrayGpu = tanh.Forward(inputArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayGpu[0].Grad = gradArrayGpu[0].Data; sw.Restart(); tanh.Backward(gradArrayGpu); sw.Stop(); Console.WriteLine("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } //Sigmoid Sigmoid sigmoid = new Sigmoid(); Console.WriteLine("\n ◆" + sigmoid.Name); sw.Restart(); gradArrayCpu = sigmoid.Forward(inputArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); sigmoid.Backward(gradArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (sigmoid.SetGpuEnable(true)) { sw.Restart(); NdArray[] gradArrayGpu = sigmoid.Forward(inputArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayGpu[0].Grad = gradArrayGpu[0].Data; sw.Restart(); sigmoid.Backward(gradArrayGpu); sw.Stop(); Console.WriteLine("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } //ReLU ReLU relu = new ReLU(); Console.WriteLine("\n ◆" + relu.Name); sw.Restart(); gradArrayCpu = relu.Forward(inputArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); relu.Backward(gradArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (relu.SetGpuEnable(true)) { sw.Restart(); NdArray[] gradArrayGpu = relu.Forward(inputArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayGpu[0].Grad = gradArrayGpu[0].Data; sw.Restart(); relu.Backward(gradArrayGpu); sw.Stop(); Console.WriteLine("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } //LeakyReLU LeakyReLU leakyRelu = new LeakyReLU(); Console.WriteLine("\n ◆" + leakyRelu.Name); sw.Restart(); gradArrayCpu = leakyRelu.Forward(inputArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); leakyRelu.Backward(gradArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (leakyRelu.SetGpuEnable(true)) { sw.Restart(); NdArray[] gradArrayGpu = leakyRelu.Forward(inputArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayGpu[0].Grad = gradArrayGpu[0].Data; sw.Restart(); leakyRelu.Backward(gradArrayGpu); sw.Stop(); Console.WriteLine("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } NdArray inputImageArrayGpu = new NdArray(BenchDataMaker.GetRealArray(3 * 256 * 256 * 5), new[] { 3, 256, 256 }, 5); NdArray inputImageArrayCpu = new NdArray(BenchDataMaker.GetRealArray(3 * 256 * 256 * 5), new[] { 3, 256, 256 }, 5); //MaxPooling MaxPooling maxPooling = new MaxPooling(3); Console.WriteLine("\n ◆" + maxPooling.Name); sw.Restart(); NdArray[] gradImageArrayCpu = maxPooling.Forward(inputImageArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradImageArrayCpu[0].Grad = gradImageArrayCpu[0].Data; sw.Restart(); maxPooling.Backward(gradImageArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (maxPooling.SetGpuEnable(true)) { sw.Restart(); maxPooling.Forward(inputImageArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); //There is no implementation for memory transfer only Console.WriteLine("Backward[Gpu] : None"); } //Conv2D Convolution2D conv2d = new Convolution2D(3, 3, 3); Console.WriteLine("\n ◆" + conv2d.Name); sw.Restart(); gradImageArrayCpu = conv2d.Forward(inputImageArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradImageArrayCpu[0].Grad = gradImageArrayCpu[0].Data; sw.Restart(); conv2d.Backward(gradImageArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (conv2d.SetGpuEnable(true)) { sw.Restart(); NdArray[] gradImageArrayGpu = conv2d.Forward(inputImageArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradImageArrayGpu[0].Grad = gradImageArrayGpu[0].Data; sw.Restart(); conv2d.Backward(gradImageArrayGpu); sw.Stop(); Console.WriteLine("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } //Deconv2D Deconvolution2D deconv2d = new Deconvolution2D(3, 3, 3); Console.WriteLine("\n ◆" + deconv2d.Name); sw.Restart(); gradImageArrayCpu = deconv2d.Forward(inputImageArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradImageArrayCpu[0].Grad = gradImageArrayCpu[0].Data; sw.Restart(); deconv2d.Backward(gradImageArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (deconv2d.SetGpuEnable(true)) { sw.Restart(); NdArray[] gradImageArrayGpu = deconv2d.Forward(inputImageArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradImageArrayGpu[0].Grad = gradImageArrayGpu[0].Data; sw.Restart(); deconv2d.Backward(gradImageArrayGpu); sw.Stop(); Console.WriteLine("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } //Dropout Dropout dropout = new Dropout(); Console.WriteLine("\n ◆" + dropout.Name); sw.Restart(); gradArrayCpu = dropout.Forward(inputArrayCpu); sw.Stop(); Console.WriteLine("Forward [Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayCpu[0].Grad = gradArrayCpu[0].Data; sw.Restart(); dropout.Backward(gradArrayCpu); sw.Stop(); Console.WriteLine("Backward[Cpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); if (dropout.SetGpuEnable(true)) { sw.Restart(); NdArray[] gradArrayGpu = dropout.Forward(inputArrayGpu); sw.Stop(); Console.WriteLine("Forward [Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); gradArrayGpu[0].Grad = gradArrayGpu[0].Data; sw.Restart(); dropout.Backward(gradArrayGpu); sw.Stop(); Console.WriteLine("Backward[Gpu] : " + (sw.ElapsedTicks / (Stopwatch.Frequency / (1000L * 1000L))).ToString("n0") + "μs"); } }